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首页> 外文期刊>International Journal of Human Factors Modelling and Simulation >Queueing network-adaptive control of thought rational (QN-ACTR): an integrated cognitive architecture for modelling complex cognitive and multi-task performance
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Queueing network-adaptive control of thought rational (QN-ACTR): an integrated cognitive architecture for modelling complex cognitive and multi-task performance

机译：排队网络对思想理性的自适应控制（QN-ACTR）：用于对复杂的认知和多任务绩效进行建模的集成认知体系结构

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摘要

How to computationally model human performance in complex cognitive and multi-task scenarios has become an important yet challenging question for human performance modelling and simulation. This paper reports the work that develops an integrated cognitive architecture for this purpose. The resulting architecture - queueing network-adaptive control of thought rational (QN-ACTR) - is an integration of the QN mathematical architecture and the ACT-R symbolic architecture. This integration allows QN-ACTR to overcome the limitations in each method and model a wider range of tasks. Implemented as a computerised simulation programme, QN-ACTR has been verified in the simulation of 20 typical tasks from the ACT-R literature. The benefits of the integration have been demonstrated in the simulation of 29 transcription typing phenomena, showing its capability in modelling complex cognitive and multi-task scenarios that have not been modelled by either QN or ACT-R.
机译：如何在复杂的认知和多任务场景中对人类绩效进行计算建模已成为人类绩效建模和仿真的一个重要但具有挑战性的问题。本文报告了为此目的开发集成认知体系结构的工作。由此产生的体系结构-队列网络对思想理性的自适应控制（QN-ACTR）-是QN数学体系结构和ACT-R符号体系结构的集成。这种集成使QN-ACTR可以克服每种方法的局限性，并可以对更广泛的任务进行建模。作为计算机模拟程序实施，QN-ACTR已在ACT-R文献的20个典型任务的模拟中得到验证。集成的好处已在29种转录类型现象的仿真中得到了证明，显示了其对复杂的认知和多任务场景进行建模的能力，这些场景尚未被QN或ACT-R建模。

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